160 citations · 170 across the 10 of their papers we have counts for
12 papers
Explaining the (Not So) Obvious: Simple and Fast Explanation of STAN, a Next Point of Interest Recommendation System
Fajrian Yunus, Talel Abdessalem
A lot of effort in recent years have been expended to explain machine learning systems. However, some machine learning methods are inherently explainable, and thus are not complete…
River: machine learning for streaming data in Python
Jacob Montiel, Max Halford, Saulo Martiello Mastelini +8
River is a machine learning library for dynamic data streams and continual learning. It provides multiple state-of-the-art learning methods, data generators/transformers, performan…
Adaptive XGBoost for Evolving Data Streams
Jacob Montiel, Rory Mitchell, Eibe Frank +3
Boosting is an ensemble method that combines base models in a sequential manner to achieve high predictive accuracy. A popular learning algorithm based on this ensemble method is e…
Novel Adaptive Algorithms for Estimating Betweenness, Coverage and k-path Centralities
Mostafa Haghir Chehreghani, Albert Bifet, Talel Abdessalem
An important index widely used to analyze social and information networks is betweenness centrality. In this paper, first given a directed network and a vertex , we…
Scikit-Multiflow: A Multi-output Streaming Framework
Jacob Montiel, Jesse Read, Albert Bifet +1
Scikit-multiflow is a multi-output/multi-label and stream data mining framework for the Python programming language. Conceived to serve as a platform to encourage democratization o…
Exact and Approximate Algorithms for Computing Betweenness Centrality in Directed Graphs
Mostafa Haghir Chehreghani, Albert Bifet, Talel Abdessalem
Graphs (networks) are an important tool to model data in different domains. Real-world graphs are usually directed, where the edges have a direction and they are not symmetric. Bet…
Metropolis-Hastings Algorithms for Estimating Betweenness Centrality in Large Networks
Mostafa Haghir Chehreghani, Talel Abdessalem, and Albert Bifet
Betweenness centrality is an important index widely used in different domains such as social networks, traffic networks and the world wide web. However, even for mid-size networks…
Discriminative Distance-Based Network Indices with Application to Link Prediction
Mostafa Haghir Chehreghani, Albert Bifet, Talel Abdessalem
In large networks, using the length of shortest paths as the distance measure has shortcomings. A well-studied shortcoming is that extending it to disconnected graphs and directed…
Social media marketing analytics : a multicultural approach applied to the beauty \& cosmetic sector
Hajer Kefi, Sitesh Indra, Talel Abdessalem
We present in this paper a multicultural approach to social media marketing analytics, applied in two Facebook brand pages: French (individualistic culture, the country home of the…
Pessimistic Uplift Modeling
Atef Shaar, Talel Abdessalem, Olivier Segard
Uplift modeling is a machine learning technique that aims to model treatment effects heterogeneity. It has been used in business and health sectors to predict the effect of a speci…
Improving tag recommendation by folding in more consistency
Modou Gueye, Talel Abdessalem, Hubert Naacke
Tag recommendation is a major aspect of collaborative tagging systems. It aims to recommend tags to a user for tagging an item. In this paper we present a part of our work in progr…
Dynamic recommender system : using cluster-based biases to improve the accuracy of the predictions
Modou Gueye, Talel Abdessalem, Hubert Naacke
It is today accepted that matrix factorization models allow a high quality of rating prediction in recommender systems. However, a major drawback of matrix factorization is its sta…